3 main facilities for producing graphics in R: base, lattice, and ggplot2
In practice, these facilities are grouped into two camps: "basic" and "advanced"
A better formulation: quick/dirty v. involved/fancy
quick/dirty v. involved/fancy
Recall that R is an object-oriented programming language
tips <-reshape2::tips # Load dataset on tipping behavior included with reshape2 packageattributes(tips) # Check attributes of the tips dataset (names, row.names, class)
# Create an object of class 'lm' (linear model), regressing tip on some# covariates
tips.reg <-lm(formula = tip ~total_bill +sex +smoker +day +time +size,
data = tips)
attributes(tips.reg) # Check attributes of the tips.reg object (names, class)
# There are many variables, so it can be helpful to extract their classes# (mostly to check for factors) via a quick for-loop
classes <-NULL# Create a placeholder for the class output# for-loop
for (i in 1:ncol(data)) {
classes <-c(classes, class(data[, i]))
}
sort(classes) # Sort the results alphabetically (appears to be some factors, a few integers, and many numerics)
Note that data-bearing arguments of length creater than 1 ("x" in this case) must be vectorized in some manner (usually using the '$' column vector method)
plot(x = vturn) # Attempt to plot variable 'vturn' without vectorization
## Error: object 'vturn' not found
plot(x = data$vturn) # Attempt to plot variable 'vturn' with '$' vectorization
Basic call takes the following form
plot(x = , y = , type ="")
base/type (scatter, line, both)
The "type" argument accepts the following character indicators
plot(x = data$year, y = data$realgdpgr, type ="p") # Plot Year on x-axis and Real GDP Growth on y-axis
"l" -- line graphs
plot(x = data$year, y = data$realgdpgr, type ="l") # Plot Year on x-axis and Real GDP Growth on y-axis
"b" -- both line and point plots
plot(x = data$year, y = data$realgdpgr, type ="b") # Plot Year on x-axis and Real GDP Growth on y-axis
base/type (histograms, density plots)
Certain plot types require different calls outside of the "type" argument
Ex) Histograms
hist(x = data$vturn) # Plot histogram of voter turnout
hist(x = data$vturn, breaks =2) # Plot histogram of voter turnout, with 2 breaks
hist(x = data$vturn, breaks =50) # Plot histogram of voter turnout, with 50 breaks
Ex) Density plots
vturn.density <-density(x = data$vturn, na.rm = T) # Create a density object (NOTE: be sure to remove missing values)class(vturn.density) # Check the class of the object
## [1] "density"
vturn.density # View the contents of the object
##
## Call:
## density.default(x = data$vturn, na.rm = T)
##
## Data: data$vturn (1134 obs.); Bandwidth 'bw' = 2.826
##
## x y
## Min. : 26.5 Min. :0.00001
## 1st Qu.: 46.0 1st Qu.:0.00248
## Median : 65.4 Median :0.00626
## Mean : 65.4 Mean :0.01285
## 3rd Qu.: 84.8 3rd Qu.:0.02472
## Max. :104.3 Max. :0.03609
plot(x = vturn.density) # Plot the density object
plot(x =density(x = data$vturn, bw =2, na.rm = T)) # Plot the density object, bandwidth of 2
plot(x =density(x = data$vturn, bw =0.5, na.rm = T)) # Plot the density object, bandwidth of 0.5
plot(x =density(x = data$vturn, bw =6, na.rm = T)) # Plot the density object, bandwidth of 6
base/options [labeling]
Basic call with popular labeling arguments
plot(x = , y = , type ="", xlab ="", ylab ="", main ="")
From the previous example
plot(x = data$year, y = data$realgdpgr, type ="p", xlab ="Year", ylab ="Real GDP Growth",
main ="This Graph is Great") # Add labels for axes and overall plot
base/options [axis + size scaling]
Basic call with popular scaling arguments
plot(x = , y = , type ="", xlim = , ylim = , cex = )
From the previous example
plot(x = data$year, y = data$realgdpgr, type ="p") # Create a basic plot
# Limit the years (x-axis) to between 1976 and 1991plot(x = data$year, y = data$realgdpgr, type ="p", xlim =c(1976, 1991))
# Limit the years (x-axis) to between 1976 and 1991, increase point size to# 2plot(x = data$year, y = data$realgdpgr, type ="p", xlim =c(1976, 1991), cex =2)
# Limit the years (x-axis) to between 1976 and 1991, decrease point size to# 0.5plot(x = data$year, y = data$realgdpgr, type ="p", xlim =c(1976, 1991), cex =0.5)
base/options [graphical parameters]
Basic call with popular scaling arguments
plot(x = , y = , type ="", col ="", pch = , lty = , lwd = )
plot(x = data$year, y = data$realgdpgr, type ="p", pch =3) # Change point style to crosses
plot(x = data$year, y = data$realgdpgr, type ="p", pch =15) # Change point style to filled squares
# Change point style to filled squares and increase point size to 3plot(x = data$year, y = data$realgdpgr, type ="p", pch =15, cex =3)
plot(x = data$year, y = data$realgdpgr, type ="p", pch ="w") # Change point style to 'w'
# Change point style to '$' and increase point size to 2plot(x = data$year, y = data$realgdpgr, type ="p", pch ="$", cex =2)
Line Styles and Widths
# Create a data.frame containing yearly average Real GDP Growth over all# countrieslibrary(plyr)
# Split-apply-combine via plyr
mean.growth <-ddply(.data = data, .variables = .(year), summarize, mean =mean(realgdpgr))
head(mean.growth)
## year mean
## 1 1960 NA
## 2 1961 5.612
## 3 1962 4.830
## 4 1963 5.310
## 5 1964 6.618
## 6 1965 5.010
# Line plot with solid lineplot(x = mean.growth$year, y = mean.growth$mean, type ="l", lty =1)
# Line plot with medium dashed lineplot(x = mean.growth$year, y = mean.growth$mean, type ="l", lty =2)
# Line plot with short dashed lineplot(x = mean.growth$year, y = mean.growth$mean, type ="l", lty =3)
# Change line width to 2plot(x = mean.growth$year, y = mean.growth$mean, type ="l", lty =3, lwd =2)
# Change line width to 3plot(x = mean.growth$year, y = mean.growth$mean, type ="l", lty =3, lwd =3)
# Change line width to 4plot(x = mean.growth$year, y = mean.growth$mean, type ="l", lty =3, lwd =4)
base/layering
Layering is accomplished by plotting succesive commands of "lines()", "points()", etc. after "plot()"
# Subset data to create a few data.frames containing data for a single# country
france.growth <-data[data$country == "France", ]
italy.growth <-data[data$country == "Italy", ]
spain.growth <-data[data$country == "Spain", ]
Successive calls to "plot()" returns two different plots
# First call to plotplot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
# Second call to plotplot(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
But using "lines()" for the second and subsequent calls layers the plots
# First call to plotplot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
# First call to lineslines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
# Second call to lineslines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
The same is true for "points()"
# First call to plotplot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
# First call to lineslines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
# Second call to lineslines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
# First call to pointspoints(x =1986, y =6, pch =13, col =colors()[116])
# Second call to pointspoints(x =1986, y = -1, pch =13, col =colors()[24])
# Third call to pointspoints(x =1966, y =2, pch =13, col =colors()[391])
# Fourth call to pointspoints(x =2008, y =4, pch =13, col =colors()[8])
plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
# First call to texttext(x =1967, y = -1, labels ="No dot here")
Reference Lines
plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
text(x =1967, y = -1, labels ="No dot here")
# First call to ablineabline(v =1980, h =0)
Legends
plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
text(x =1967, y = -1, labels ="No dot here")
abline(v =1980, h =0)
# First call to legend (note the vector position correspondence between each# of the argument values)legend("topright", legend =c("France", "Italy", "Spain"), col =c("red", "blue",
"darkgreen"), lty =c(1, 1, 1), lwd =c(2, 2, 2), cex =0.8)
base/tables
Can form tables of graphs using the "par()" call like so:
par(mrow =c(ncols, nrows))
# STEP 1: Call 'par() for a 2x2 table'par(mfrow =c(2, 2))
# STEP 2: Plot #1plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
# STEP 3: Plot #2plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
text(x =1967, y = -1, labels ="No dot here")
# STEP 4: Plot #3plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
text(x =1967, y = -1, labels ="No dot here")
abline(v =1980, h =0)
# STEP 5: Plot #4plot(x = france.growth$year, y = france.growth$realgdpgr, type ="l", col ="red",
lwd =2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type ="l", col ="blue",
lwd =2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type ="l", col ="darkgreen",
lwd =2)
points(x =1986, y =6, pch =13, col =colors()[116])
points(x =1986, y = -1, pch =13, col =colors()[24])
points(x =1966, y =2, pch =13, col =colors()[391])
points(x =2008, y =4, pch =13, col =colors()[8])
text(x =1967, y = -1, labels ="No dot here")
abline(v =1980, h =0)
legend("topright", legend =c("France", "Italy", "Spain"), col =c("red", "blue",
"darkgreen"), lty =c(1, 1, 1), lwd =c(2, 2, 2), cex =0.8)
Out with the old...
Base graphics are really great, but they're not like this (created with a single line of code)
lattice (Deepayan Sarkar, ISI, Delhi)
ggplot2 (Hadley Wickham, again)
Both are built on "grid", both are really huge improvements over base R graphics
Both also have entire books written about them (~200-300 pp. each)
lattice v. ggplot2
lattice is
faster (though only noticeable over many and large plots)
simpler (at first)
better at trellis graphs
able to do 3d graphs
ggplot2 is
generally more elegant
more syntactically logical (and therefore simpler, once you learn it)
better at grouping
able to interface with maps
Basic usage: lattice
The general call for lattice graphics looks something like this:
graph_type(formula, data=, [options])
The specifics of the formula differ for each graph type, but the general format is straightforward
y # Show the distribution of y
y ~x # Show the relationship between x and y
y ~x |A # Show the relationship between x and y conditional on the values of A
y ~x |A *B # Show the relationship between x and y conditional on the combinations of A and B
z ~y *x # Show the 3D relationship between x, y, and z
Basic usage: ggplot2
The general call for ggplot2 graphics looks something like this:
# Create data.frame of average growth rates by country over time
growth <-ddply(.data = data, .variables = .(country), summarize, mean =mean(realgdpgr,
na.rm = T))
barchart(mean ~country, data = growth) # lattice
ggplot(data = volcano3d, aes(x = xvar, y = yvar, z = zvar)) +geom_contour() # ggplot2
lattice v. ggplot2: tile/image/level plots
levelplot(zvar ~xvar +yvar, data = volcano3d) # lattice
ggplot(data = volcano3d, aes(x = xvar, y = yvar, z = zvar)) +geom_tile(aes(fill = zvar)) # ggplot2
lattice: 3D plots
# Create a subset of the dataset containing only data for France
france.data <-data[data$country == "France", ]
cloud(outlays ~year *vturn, data = france.data)
# Create a subset of the dataset containing only data for Greece, Portugal,# Ireland, and Spain
pigs.data <-data[data$country %in%c("Greece", "Portugal", "Ireland", "Spain"),
]
cloud(outlays ~year *vturn |country, data = pigs.data)
ggplot2: Panel plots
ggplot(data = pigs.data, aes(x = year, y = vturn, color = country)) +geom_line()
lattice v. ggplot2: options [labeling]
xyplot(outlays ~vturn, data = data, xlab ="Voter Turnout (%)", ylab ="Government Outlays",
main ="This Graph is Also Great") # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) +geom_point() +xlab(label ="Voter Turnout (%)") +ylab(label ="Government Outlays") +ggtitle(label ="This Graph is Also Also Great") # ggplot2
lattice v. ggplot2: options [axis + size scaling]
xyplot(outlays ~vturn, data = data, xlim =c(80, 100)) # lattice
By now, you might be noticing some trends in how these two packages approach graphics
lattice tends to focus on a particular type of graph and how to represent cross-sectional variation by splitting it up into smaller chunks
Becoming a proficient user of lattice requires learning a huge array of graph-specific formulas and options
ggplot2 tries to represent much more of the cross-sectional variation by making use of various "aesthetics"; general approach is based on The Grammar of Graphics
ggplot2 and the Grammar of Graphics
Basic idea is that the visualization of all data requires four items
One or more statistics conveying information about the data (identities, means, medians, etc.)
A coordinate system that differentiates between the intersections of statistics (at most two for ggplot, three for lattice)
Geometries that differentiate between off-coordinate variation in kind
Scales that differentiate between off-coordinate variation in degree
ggplot2 allows the user to manipulate all four of these items
ggplot2 is optimized for showing variation on all four aesthetic types
# Differences in kind using colorggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_line(aes(color = country))
Note what happens when we specify the color parameter outside of the aesthetic operator. ggplot2 views these specifications as invalid graphical parameters.
# Differences in kind using line typesggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_line(aes(linetype = country))
# Differences in kind using point shapesggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point(aes(shape = country))
# Differences in degree using colorggplot(data = pigs.data, aes(x = year, y = realgdpgr)) +geom_point(aes(color = vturn))
# Differences in degree using point sizeggplot(data = pigs.data, aes(x = year, y = realgdpgr)) +geom_point(aes(size = vturn))
# Multiple non-cartesian aesthetics (differences in kind using color, degree# using point size)ggplot(data = pigs.data, aes(x = year, y = realgdpgr)) +geom_point(aes(color = country,
size = vturn))
Fitted lines and curves with ggplot2
ggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point()
# Add linear model (lm) smootherggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point() +geom_smooth(method ="lm")
# Add local linear model (loess) smoother, span of 0.75ggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point() +geom_smooth(method ="loess",
span =0.75)
# Add local linear model (loess) smoother, span of 0.25ggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point() +geom_smooth(method ="loess",
span =0.25)
# Add linear model (lm) smoother, no standard error shadingggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point() +geom_smooth(method ="lm",
se = F)
# Add local linear model (loess) smoother, no standard error shadingggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point() +geom_smooth(method ="loess",
se = F)
# Add a local linear (loess) smoother for each countryggplot(data = pigs.data, aes(x = year, y = vturn)) +geom_point(aes(color = country)) +geom_smooth(aes(color = country))
# Add a local linear (loess) smoother for each country, no standard error# shadingggplot(data = pigs.data, aes(x = year, y = realgdpgr)) +geom_point(aes(color = country,
size = vturn)) +geom_smooth(aes(color = country), se = F)
lattice v. ggplot2: tables
Both lattice and ggplot2 graphs can be combined using the grid.arrange() function in the gridExtra package
# Initialize gridExtra librarylibrary(gridExtra)
# Create 3 plots to combine in a table
plot1 <-ggplot(data = pigs.data, aes(x = year, y = vturn, color = )) +geom_line(aes(color = country))
plot2 <-ggplot(data = pigs.data, aes(x = year, y = vturn, linetype = )) +geom_line(aes(linetype = country))
plot3 <-ggplot(data = pigs.data, aes(x = year, y = vturn, shape = )) +geom_point(aes(shape = country))
# Call grid.arrangegrid.arrange(plot1, plot2, plot3, nrow =3, ncol =1)
Exporting
Two basic image types
Raster/Bitmap (.png, .jpeg)
Every pixel of a plot contains its own separate coding; not so great if you want to resize the image
